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Bootstrap-Based Improvements for Inference with Clustered Errors

A. Colin Cameron, Jonah B. Gelbach and Douglas Lee Miller ()

The Review of Economics and Statistics, 2008, vol. 90, issue 3, pages 414-427

Abstract: Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Date: 2008
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Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2007) Downloads
Working Paper: Bootstrap-Based Improvements for Inference with Clustered Errors (2006) Downloads
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